The threat revolves around the phenomenon where AI-based code assistants, such as ChatGPT, hallucinate or suggest non-existent software packages during development. Attackers can exploit this by preemptively registering these fictitious package names on public repositories like RubyGems, PyPI, or Go modules, embedding malicious code within them. This attack vector is a variant of supply chain attacks known as slopsquatting, where attackers rely on typosquatting or namespace confusion to trick developers into installing compromised dependencies. The risk is exacerbated by the increasing reliance on AI tools for coding, which have been shown to hallucinate package names 5-21% of the time. If a developer blindly trusts these AI suggestions and publishes code referencing these packages, it can lead to the inadvertent installation of malware during build or deployment processes. To address this, the open-source tool SlopGuard was developed. SlopGuard performs a three-stage trust scoring process to verify the existence of packages in registries, detect typosquatting and namespace attacks, and scan large sets of packages rapidly with a low false positive rate (2.7%) and high detection efficacy (96%) on known supply chain attacks. It supports major package ecosystems including RubyGems, PyPI, and Go modules. Although no known exploits have been reported in the wild, the threat is significant due to the potential for remote code execution and the difficulty in detecting such attacks without dedicated tooling. The tool is implemented in Ruby, MIT licensed, and publicly available on GitHub, encouraging adoption and further research. This threat highlights a new dimension of supply chain risk introduced by AI-assisted development and the need for enhanced verification mechanisms in software supply chains.

For European organizations, the impact of this threat lies primarily in the compromise of software supply chains, which can lead to the introduction of malicious code into production environments. This can result in unauthorized remote code execution, data breaches, and disruption of services. Organizations heavily reliant on open-source packages from RubyGems, PyPI, or Go modules are particularly vulnerable. The risk is amplified in sectors with stringent regulatory requirements around data protection and software integrity, such as finance, healthcare, and critical infrastructure. The inadvertent installation of malicious dependencies could lead to compliance violations under GDPR and other regulations, reputational damage, and financial losses. Additionally, the threat exploits the growing trend of AI-assisted development, which is becoming common in European software projects, increasing the attack surface. The supply chain nature of the attack means that even organizations with strong perimeter defenses can be compromised if malicious packages are introduced during the build process. The medium severity rating reflects that while exploitation requires developer interaction, the consequences of a successful attack can be severe, including full system compromise. European organizations must therefore consider this threat in their software development lifecycle and supply chain risk management strategies.

European organizations should adopt a multi-layered approach to mitigate this threat. First, integrate package verification tools like SlopGuard into CI/CD pipelines to automatically validate that all dependencies exist in official registries and are not typosquatted or malicious. Second, enforce strict policies requiring developers to verify AI-generated code suggestions before use, including manual review of new dependencies. Third, maintain an allowlist of approved packages and versions to prevent unauthorized dependencies from being introduced. Fourth, monitor package registries for suspicious new packages that mimic internal or popular package names and consider using automated alerts for such events. Fifth, educate development teams about the risks of AI hallucinations and supply chain attacks to foster security-aware coding practices. Sixth, employ software composition analysis (SCA) tools that can detect unusual or new dependencies and flag potential risks. Finally, collaborate with package registry maintainers and the open-source community to report and remove malicious packages promptly. These steps go beyond generic advice by focusing on integrating detection tools, developer education, and proactive monitoring tailored to the AI-assisted development context.